Triple
T61827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United Nations Development Programme |
E1228
|
entity |
| Predicate | numberOfCountryOffices |
P4564
|
FINISHED |
| Object | about 170 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: about 170 | Statement: [United Nations Development Programme, numberOfCountryOffices, about 170]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfCountryOffices Context triple: [United Nations Development Programme, numberOfCountryOffices, about 170]
-
A.
numberOfRegions
Indicates the total count of distinct regions associated with or contained within a given entity.
-
B.
hasNumberOfMemberInstitutions
Indicates the quantitative count of member institutions associated with a given entity.
-
C.
numberOfDistricts
Indicates the total count of districts associated with a given entity or area.
-
D.
numberOfMemberStates
Indicates the total count of member states associated with a given entity or organization.
-
E.
numberOfCampuses
Indicates the total count of campuses associated with a given entity.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a24ba4f760819081f6638a3c70538a |
completed | Feb. 28, 2026, 1:57 a.m. |
| NER | Named-entity recognition | batch_69a251f74b0881909ad89127b8171277 |
completed | Feb. 28, 2026, 2:24 a.m. |
| PD | Predicate disambiguation | batch_69a24ea242c8819086fe00bf01e6523e |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a251f6786081908eaaed6190695322 |
completed | Feb. 28, 2026, 2:24 a.m. |
Created at: Feb. 28, 2026, 2:02 a.m.